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71c1ad2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 | # app/models/text_model.py
# RoBERTa-based text toxicity model with ONNX optimization
from pathlib import Path
import numpy as np
from app.config import get_settings
from app.observability.logging import get_logger
logger = get_logger(__name__)
class TextToxicityModel:
"""
Text toxicity classifier using a RoBERTa-based model.
Supports both ONNX (fast) and PyTorch (fallback) inference.
Model: unitary/toxic-bert (multi-label toxicity detection).
Labels: toxic, severe_toxic, obscene, threat, insult, identity_hate
"""
LABELS = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]
def __init__(self):
self.settings = get_settings()
self.tokenizer = None
self.onnx_session = None
self.pt_model = None
self.device = None
self._loaded = False
def load(self) -> None:
"""Load the tokenizer and model (ONNX preferred, PyTorch fallback)."""
from transformers import AutoTokenizer
model_name = self.settings.text_model_name
cache_dir = self.settings.model_cache_path / "roberta"
onnx_path = cache_dir / "text_toxicity.onnx"
logger.info("loading_text_model", model=model_name)
# Load tokenizer
self.tokenizer = AutoTokenizer.from_pretrained(model_name, cache_dir=cache_dir)
if self.settings.onnx_enabled and onnx_path.exists():
# Use existing ONNX model
from app.models.onnx_utils import load_onnx_session
self.onnx_session = load_onnx_session(onnx_path)
logger.info("text_model_loaded", backend="onnx")
elif self.settings.onnx_enabled:
# Load PyTorch, export to ONNX, then use ONNX
self._load_pytorch(model_name, cache_dir)
self._export_onnx(onnx_path)
# Switch to ONNX session
from app.models.onnx_utils import load_onnx_session
self.onnx_session = load_onnx_session(onnx_path)
self.pt_model = None # Free PyTorch memory
logger.info("text_model_loaded", backend="onnx", note="exported_from_pytorch")
else:
# PyTorch only
self._load_pytorch(model_name, cache_dir)
logger.info("text_model_loaded", backend="pytorch")
self._loaded = True
def _load_pytorch(self, model_name: str, cache_dir: Path) -> None:
"""Load the PyTorch model."""
import torch
from transformers import AutoModelForSequenceClassification
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.pt_model = AutoModelForSequenceClassification.from_pretrained(
model_name, cache_dir=cache_dir
)
self.pt_model.to(self.device)
self.pt_model.eval()
def _export_onnx(self, onnx_path: Path) -> None:
"""Export current PyTorch model to ONNX."""
import torch
from app.models.onnx_utils import export_to_onnx
sample = self.tokenizer(
"test input for export",
return_tensors="pt",
padding="max_length",
truncation=True,
max_length=128,
)
sample = {k: v.to(self.device) for k, v in sample.items()}
export_to_onnx(
model=self.pt_model,
sample_input=sample,
output_path=onnx_path,
input_names=["input_ids", "attention_mask"],
output_names=["logits"],
)
def predict(self, text: str) -> dict:
"""
Predict toxicity scores for input text.
Args:
text: Input text to classify.
Returns:
Dict with:
- labels: list of label names
- scores: list of per-label probabilities
- is_toxic: bool (any label > 0.5)
- max_score: float (highest toxicity probability)
- max_label: str (label with highest probability)
"""
if not self._loaded:
raise RuntimeError("Text model not loaded. Call load() first.")
# Tokenize
encoding = self.tokenizer(
text,
return_tensors="np" if self.onnx_session else "pt",
padding="max_length",
truncation=True,
max_length=128,
)
if self.onnx_session:
return self._predict_onnx(encoding)
else:
return self._predict_pytorch(encoding)
def _predict_onnx(self, encoding: dict) -> dict:
"""Run ONNX inference."""
from app.models.onnx_utils import onnx_inference
inputs = {
"input_ids": encoding["input_ids"].astype(np.int64),
"attention_mask": encoding["attention_mask"].astype(np.int64),
}
outputs = onnx_inference(self.onnx_session, inputs)
logits = outputs[0][0] # (num_labels,)
return self._format_output(logits)
def _predict_pytorch(self, encoding: dict) -> dict:
"""Run PyTorch inference."""
import torch
inputs = {k: v.to(self.device) for k, v in encoding.items()}
with torch.no_grad():
outputs = self.pt_model(**inputs)
logits = outputs.logits[0].cpu().numpy()
return self._format_output(logits)
def _format_output(self, logits: np.ndarray) -> dict:
"""Convert raw logits to formatted prediction dict."""
# Sigmoid for multi-label classification
scores = 1 / (1 + np.exp(-logits))
scores = scores.tolist()
# Handle case where model has fewer outputs than expected labels
labels = self.LABELS[: len(scores)]
label_scores = dict(zip(labels, scores))
max_idx = int(np.argmax(scores))
is_toxic = any(s > 0.5 for s in scores)
return {
"labels": labels,
"scores": scores,
"label_scores": label_scores,
"is_toxic": is_toxic,
"max_score": scores[max_idx],
"max_label": labels[max_idx],
}
@property
def is_loaded(self) -> bool:
return self._loaded
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